Abstract

Hypoglycemia and hyperglycemia in critically ill patients have been considered as being closely tied to mortality. However, clinical researchers and physicians have suspected that this relationship may be confounded by other time-updated biomarkers such as the acute physiology and the chronic health evaluation (APACHE) score which is a measurement of disease severity. To investigate the effect of dysglycemia on mortality while adjusting for time-dependent biomarkers, we first need to ensure that these time-updated biomarkers are associated with the main exposure variable, i.e., the time-dependent glucose levels.

Several researchers have proposed methods to estimate the true correlation coefficient between two repeatedly measured continuous variables using the maximum likelihood method via the mixed effects modeling with an assumption that these two variables are measured at the same time points. In this study, we further extend the methods proposed by these researchers, and proposed a method that can be used to examine the correlation coefficients among multiple (two or more) variables measured repeatedly. The method we proposed can be applied to linked cases where repeated measurements are linked over time and to unlinked cases where measurements are not necessarily measured concurrently.

The dataset we used for demonstration is the HighDensity ICU dataset, an electronic dataset from an eight-year observational cohort of more than 54,000 admissions recorded in twelve Intensive care units (ICUs) of a large tertiary care center. Several risk factors of mortality were recorded daily for the ICU admissions including glucose level, disease severity score, organ dysfunction scores, cumulative daily insulin doses, and caloric intake.

Simulation studies were conducted to examine the empirical features of our proposed method under different underlying scenarios. In application, we compared our method with the methods proposed by previous researchers on correlation coefficients between any two risk factors.

Public Health Significance: One of the objectives of the current on-going study is to find the relationship between the glucose level and the APACHE III score, both measured repeatedly, while controlling for other repeatedly measured biomarkers. Findings in this study not only can provide helpful information for physicians in the ICUs to optimize treatments in critically ill patients with septic shock, but also can provide biostatisticians a better statistical tool to estimate correlation between two longitudinally measured biomarkers that are adjusted for within-subject correlation while controlling for repeatedly measured confounding factors.